As known, the methods for recognizing and locating objects play very important roles in machine vision. Before the procedure of measuring and detecting the under-test material in the production line, it is necessary to recognize and locate the position of the under-test material in order to compensate the placement position error of the under-test material. Moreover, for gripping and assembling the material in the production line by a robot, it is necessary to recognize and locate the material.
In a conventional object identification and location method, a connected component analysis is employed. Firstly, a foreground of a binary image is obtained. Then, the axis length, the area, the perimeter or any other appropriate feature of the foreground is analyzed. Consequently, the object can be recognized. However, if the object has a broken part or a hidden part, the recognition rate of the connected component analysis is largely reduced. Moreover, if the change of the ambient light intensity is very obvious, it is difficult to segment the binary image of the object clearly.
Moreover, the sum of absolute differences (SAD) is an algorithm that is widely used to locate the object. This SAD algorithm does not need the binary operations. Moreover, if the concepts of a triangle inequality described by Salari, W. Li and E. are applied to the SAD algorithm, the amount of the data to be calculated is largely reduced. However, the SAD algorithm fails to overcome the problem caused by the linear light intensity variation. Recently, a normalized cross correlation (NCC) method described by Luigi Di Stefano, Stefano Mattoccia and Martino Mola can overcome the problem caused by the linear light intensity variation. Consequently, the NCC method becomes one of the most popular locating methods. However, if the object is hidden or a non-linear light intensity variation exists, the similarity score is largely reduced. Under this circumstance, an unstable location problem occurs.
The above conventional methods use pixels as the location features. Recently, a generalizing Hough transform (GHT) described by D. H. Ballard extracts edge points from a grayscale image. That is, the edge point is used as the geometric feature. The GHT method can overcome the problems of the non-linear light intensity variation and the hidden object. However, if the background of the image is complicate and the number of the non-edge parts of the object is huge, the votes for the location of the object produce many false positives. In other words, the detection result is unstable. Moreover, a chamfer transform described by Thayananthan, A., Stenger, B., Ton, P. H. S. and Cipolla, R. uses the edge points as the feature for location. The chamfer transform can accelerate the location of the object. However, if the object has a hidden part and many non-edge parts of the object are generated, the locating efficacy is impaired.
Therefore, there is a need of providing a method for effectively recognizing and locating an object in order to overcome the above drawbacks.